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 ## Overview
 **watsonxDocsQA**  is a new open-source dataset and benchmark contributed by IBM. The dataset is derived from enterprise product documentation and designed specifically for end-to-end Retrieval-Augmented Generation (RAG) evaluation. The dataset consists of two components:
-- **Documents**: A corpus of 1,144 text and markdown files generated by crawling enterprise documentation ([main page](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html)).
 - **Benchmark**: A set of 75 question-answer (QA) pairs with gold document labels and answers.The QA pairs are crafted as follows:
   - **25 questions**: Human-generated by two subject matter experts.
   - **50 questions**: Synthetically generated using the `tiiuae/falcon-180b` model, then manually filtered and reviewed for quality. The methodology is detailed in [Yehudai et al. 2024](https://arxiv.org/pdf/2401.14367).

 ## Overview
 **watsonxDocsQA**  is a new open-source dataset and benchmark contributed by IBM. The dataset is derived from enterprise product documentation and designed specifically for end-to-end Retrieval-Augmented Generation (RAG) evaluation. The dataset consists of two components:
+- **Documents**: A corpus of 1,144 text and markdown files generated by crawling enterprise documentation ([main page - crawl March 2024](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html)).
 - **Benchmark**: A set of 75 question-answer (QA) pairs with gold document labels and answers.The QA pairs are crafted as follows:
   - **25 questions**: Human-generated by two subject matter experts.
   - **50 questions**: Synthetically generated using the `tiiuae/falcon-180b` model, then manually filtered and reviewed for quality. The methodology is detailed in [Yehudai et al. 2024](https://arxiv.org/pdf/2401.14367).